Update app.py

app.py

@@ -33,6 +33,24 @@ def clear_cache():
         print(f"Error clearing cache: {e}")
         return f"Error clearing cache: {e}"
 
+PREDEFINED_QUERIES = {
+    "Recent Earnings": {
+        "query": "{company} recent quarterly earnings",
+        "instructions": "Provide the most recent quarterly earnings data for {company}. Include revenue, net income, and EPS. Specify the exact quarter and year."
+    },
+    "Recent News": {
+        "query": "{company} recent news",
+        "instructions": "Summarize the most recent significant news about {company}. Focus on events that could impact the company's financial performance or stock price."
+    },
+    "Credit Rating": {
+        "query": "{company} current credit rating",
+        "instructions": "Provide the most recent credit rating for {company}. Include the rating agency, the exact rating, and the date it was issued or last confirmed."
+    },
+    "Earnings Call Transcript": {
+        "query": "{company} most recent earnings call transcript",
+        "instructions": "Summarize key points from {company}'s most recent earnings call. Include date of the call, major financial highlights, and any significant forward-looking statements."
+    }
+}
 _useragent_list = [
     "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
     "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
@@ -131,24 +149,33 @@ def format_prompt(query, search_results, instructions):
         else:
             formatted_results += "No link found.\n" + '-' * 80 + '\n'
 
-    prompt = f"{instructions}User Query: {query}\n\nWeb Search Results:\n{formatted_results}\n\nAssistant:"
+    prompt = f"""Instructions: {instructions}
+User Query: {query}
+
+Web Search Results:
+{formatted_results}
+
+Important: Provide a precise and factual response based solely on the information given above. Include specific dates, numbers, and sources where available. If exact information is not provided in the search results, clearly state that the information is not available in the given context. Do not make assumptions or provide information that is not directly supported by the search results.
+
+Assistant:"""
     return prompt
 
 # Function to generate text using Hugging Face API
-def generate_text(input_text, temperature=0.7, repetition_penalty=1.0, top_p=0.9):
+def generate_text(input_text, temperature=0.3, repetition_penalty=1.2, top_p=0.9):
     print("Generating text using Hugging Face API...")
     endpoint = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
     headers = {
-        "Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}",  # Use the environment variable
+        "Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}",
         "Content-Type": "application/json"
     }
     data = {
         "inputs": input_text,
         "parameters": {
-            "max_new_tokens": 8000,  # Adjust as needed
+            "max_new_tokens": 1000,  # Reduced to focus on more concise answers
             "temperature": temperature,
             "repetition_penalty": repetition_penalty,
-            "top_p": top_p
+            "top_p": top_p,
+            "do_sample": True
         }
     }
 
@@ -275,17 +302,24 @@ def scrape_and_display(query, num_results, instructions, web_search=True, temper
     return generated_summary
 
 # Main Gradio interface function
-def gradio_interface(query, use_pdf, pdf, num_results, instructions, temperature, repetition_penalty, top_p, clear_cache_flag):
+def gradio_interface(query, use_dashboard, use_pdf, pdf, num_results, custom_instructions, temperature, repetition_penalty, top_p, clear_cache_flag):
     if clear_cache_flag:
         return clear_cache()
     
-    if use_pdf and pdf is not None:
+    if use_dashboard:
+        results = []
+        for query_type, query_info in PREDEFINED_QUERIES.items():
+            formatted_query = query_info['query'].format(company=query)
+            formatted_instructions = query_info['instructions'].format(company=query)
+            result = scrape_and_display(formatted_query, num_results=num_results, instructions=formatted_instructions, web_search=True, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
+            results.append(f"**{query_type}**\n\n{result}\n\n")
+        generated_summary = "\n".join(results)
+    elif use_pdf and pdf is not None:
         pdf_text = read_pdf(pdf)
-        generated_summary = scrape_and_display(pdf_text, num_results=0, instructions=instructions, web_search=False, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
+        generated_summary = scrape_and_display(pdf_text, num_results=0, instructions=custom_instructions, web_search=False, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
     else:
-        generated_summary = scrape_and_display(query, num_results=num_results, instructions=instructions, web_search=True, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
+        generated_summary = scrape_and_display(query, num_results=num_results, instructions=custom_instructions, web_search=True, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
     
-    # Save the generated summary to a PDF
     output_pdf_path = "output_summary.pdf"
     save_text_to_pdf(generated_summary, output_pdf_path)
     
@@ -295,18 +329,19 @@ def gradio_interface(query, use_pdf, pdf, num_results, instructions, temperature
 gr.Interface(
     fn=gradio_interface,
     inputs=[
-        gr.Textbox(label="Query"),
+        gr.Textbox(label="Company Name or Query"),
+        gr.Checkbox(label="Use Dashboard"),
         gr.Checkbox(label="Use PDF"),
         gr.File(label="Upload PDF"),
         gr.Slider(minimum=1, maximum=20, value=5, step=1, label="Number of Results"),
-        gr.Textbox(label="Instructions"),
+        gr.Textbox(label="Custom Instructions"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Repetition Penalty"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top p"),
-        gr.Checkbox(label="Clear Cache", visible=False)  # Hidden checkbox for clear cache functionality
+        gr.Checkbox(label="Clear Cache", visible=False)
     ],
     outputs=["text", gr.File(label="Generated PDF")],
     title="Financial Analyst AI Assistant",
-    description="Enter your query about a company's financials to get valuable insights. Optionally, upload a PDF for analysis. Please instruct me for curating your output template. For web search, you can modify my search results but it's advisable to restrict them to 10. You can also adjust parameters like Temperature, Repetition Penalty, and Top_P. It's advisable to set repetition penalty at 1 and other two parameters at 0.1.",
+    description="Enter a company name to get a financial dashboard, or enter a custom query. Optionally, upload a PDF for analysis. Adjust parameters as needed for optimal results.",
     allow_flagging="never"
 ).launch(share=True)